


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						(DDIMParallelScheduler,)
    __magic_name__         =						(('eta', 0.0), ('num_inference_steps', 50))
    def 						a_     (			self     ,   **__snake_case       ):
         snake_case											=							{
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''clip_sample''': True,
         }

         config.update(**__snake_case       )
         return config
    def 						a_     (			self     ,   **__snake_case       ):
         snake_case											=							self.scheduler_classes[0]
         snake_case											=							self.get_scheduler_config(**__snake_case       )
         snake_case											=							scheduler_class(**__snake_case       )

         snake_case					,    snake_case											=							1_0, 0.0

         snake_case											=							self.dummy_model()
         snake_case											=							self.dummy_sample_deter

         scheduler.set_timesteps(__snake_case       )

         for t in scheduler.timesteps:
              snake_case											=							model(__snake_case     ,   __snake_case       )
              snake_case											=							scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       ).prev_sample

         return sample
    def 						a_     (			self       ):
         for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=__snake_case       )
    def 						a_     (			self       ):
         for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=__snake_case       )

         snake_case											=							self.scheduler_classes[0]
         snake_case											=							self.get_scheduler_config(steps_offset=1       )
         snake_case											=							scheduler_class(**__snake_case       )
         scheduler.set_timesteps(5       )
         assert torch.equal(scheduler.timesteps     ,   torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]       )       )
    def 						a_     (			self       ):
         for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]     ,   [0.002, 0.02, 0.2, 2]       ):
              self.check_over_configs(beta_start=__snake_case     ,   beta_end=__snake_case       )
    def 						a_     (			self       ):
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=__snake_case       )
    def 						a_     (			self       ):
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__snake_case       )
    def 						a_     (			self       ):
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=__snake_case       )
    def 						a_     (			self       ):
         for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=__snake_case       )
    def 						a_     (			self       ):
         for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=__snake_case       )
    def 						a_     (			self       ):
         self.check_over_configs(thresholding=__snake_case       )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__snake_case     ,   prediction_type=__snake_case     ,   sample_max_value=__snake_case     ,   )
    def 						a_     (			self       ):
         for t in [1, 1_0, 4_9]:
              self.check_over_forward(time_step=__snake_case       )
    def 						a_     (			self       ):
         for t, num_inference_steps in zip([1, 1_0, 5_0]     ,   [1_0, 5_0, 5_0_0]       ):
              self.check_over_forward(time_step=__snake_case     ,   num_inference_steps=__snake_case       )
    def 						a_     (			self       ):
         for t, eta in zip([1, 1_0, 4_9]     ,   [0.0, 0.5, 1.0]       ):
              self.check_over_forward(time_step=__snake_case     ,   eta=__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.scheduler_classes[0]
         snake_case											=							self.get_scheduler_config()
         snake_case											=							scheduler_class(**__snake_case       )

         assert torch.sum(torch.abs(scheduler._get_variance(0     ,   0       ) - 0.0       )       ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_2_0     ,   4_0_0       ) - 0.1_4771       )       ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_8_0     ,   9_6_0       ) - 0.3_2460       )       ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(0     ,   0       ) - 0.0       )       ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7     ,   4_8_6       ) - 0.0_0979       )       ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9     ,   9_9_8       ) - 0.02       )       ) < 1E-5
    def 						a_     (			self       ):
         snake_case											=							self.scheduler_classes[0]
         snake_case											=							self.get_scheduler_config()
         snake_case											=							scheduler_class(**__snake_case       )

         snake_case					,    snake_case											=							1_0, 0.0
         scheduler.set_timesteps(__snake_case       )

         snake_case											=							self.dummy_model()
         snake_case											=							self.dummy_sample_deter
         snake_case											=							self.dummy_sample_deter + 0.1
         snake_case											=							self.dummy_sample_deter - 0.1

         snake_case											=							samplea.shape[0]
         snake_case											=							torch.stack([samplea, samplea, samplea]     ,   dim=0       )
         snake_case											=							torch.arange(__snake_case       )[0:3, None].repeat(1     ,   __snake_case       )

         snake_case											=							model(samples.flatten(0     ,   1       )     ,   timesteps.flatten(0     ,   1       )       )
         snake_case											=							scheduler.batch_step_no_noise(__snake_case     ,   timesteps.flatten(0     ,   1       )     ,   samples.flatten(0     ,   1       )     ,   __snake_case       )

         snake_case											=							torch.sum(torch.abs(__snake_case       )       )
         snake_case											=							torch.mean(torch.abs(__snake_case       )       )

         assert abs(result_sum.item() - 1147.7904       ) < 1E-2
         assert abs(result_mean.item() - 0.4982       ) < 1E-3
    def 						a_     (			self       ):
         snake_case											=							self.full_loop()

         snake_case											=							torch.sum(torch.abs(__snake_case       )       )
         snake_case											=							torch.mean(torch.abs(__snake_case       )       )

         assert abs(result_sum.item() - 172.0067       ) < 1E-2
         assert abs(result_mean.item() - 0.22_3967       ) < 1E-3
    def 						a_     (			self       ):
         snake_case											=							self.full_loop(prediction_type='''v_prediction'''       )

         snake_case											=							torch.sum(torch.abs(__snake_case       )       )
         snake_case											=							torch.mean(torch.abs(__snake_case       )       )

         assert abs(result_sum.item() - 52.5302       ) < 1E-2
         assert abs(result_mean.item() - 0.0684       ) < 1E-3
    def 						a_     (			self       ):
         # We specify different beta, so that the first alpha is 0.99
         snake_case											=							self.full_loop(set_alpha_to_one=__snake_case     ,   beta_start=0.01       )
         snake_case											=							torch.sum(torch.abs(__snake_case       )       )
         snake_case											=							torch.mean(torch.abs(__snake_case       )       )

         assert abs(result_sum.item() - 149.8295       ) < 1E-2
         assert abs(result_mean.item() - 0.1951       ) < 1E-3




    def 						a_     (			self       ):
         # We specify different beta, so that the first alpha is 0.99
         snake_case											=							self.full_loop(set_alpha_to_one=__snake_case     ,   beta_start=0.01       )
         snake_case											=							torch.sum(torch.abs(__snake_case       )       )
         snake_case											=							torch.mean(torch.abs(__snake_case       )       )

         assert abs(result_sum.item() - 149.0784       ) < 1E-2
         assert abs(result_mean.item() - 0.1941       ) < 1E-3








from functools import lru_cache
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							2
     snake_case											=							set()
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.add(UpperCamelCase_							)
     if n > 1:
          factors.add(UpperCamelCase_							)
     return factors
@lru_cache
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     return len(unique_prime_factors(UpperCamelCase_							)							)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     return len(set(UpperCamelCase_							)							) in (0, 1)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							2

     while True:
          # Increment each value of a generated range
          snake_case											=							[base + i for i in range(UpperCamelCase_							)]

          # Run elements through out unique_prime_factors function
          # Append our target number to the end.
          snake_case											=							[upf_len(UpperCamelCase_							) for x in group]
          checker.append(UpperCamelCase_							)

          # If all numbers in the list are equal, return the group variable.
          if equality(UpperCamelCase_							):
               return group

          # Increment our base variable by 1
          base += 1
def      UpperCAmelCase__		(UpperCamelCase_ = 4							):



     """simple docstring"""






     snake_case											=							run(UpperCamelCase_							)
     return results[0] if len(UpperCamelCase_							) else None


if __name__ == "__main__":
 print(solution())




